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Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study.

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Date
2019-01
Authors
Sheng, Yang
Zhang, Jiahan
Wang, Chunhao
Yin, Fang-Fang
Wu, Q Jackie
Ge, Yaorong
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Abstract
Knowledge models in radiotherapy capture the relation between patient anatomy and dosimetry to provide treatment planning guidance. When treatment schemes evolve, existing models struggle to predict accurately. We propose a case-based reasoning framework designed to handle novel anatomies that are of same type but vary beyond original training samples. A total of 105 pelvic intensity-modulated radiotherapy cases were analyzed. Eighty cases were prostate cases while the other 25 were prostate-plus-lymph-node cases. We simulated 4 scenarios: Scarce scenario, Semiscarce scenario, Semiample scenario, and Ample scenario. For the Scarce scenario, a multiple stepwise regression model was trained using 85 cases (80 prostate, 5 prostate-plus-lymph-node). The proposed workflow started with evaluating the feature novelty of new cases against 5 training prostate-plus-lymph-node cases using leverage statistic. The case database was composed of a 5-case dose atlas. Case-based dose prediction was compared against the regression model prediction using sum of squared residual. Mean sum of squared residual of case-based and regression predictions for the bladder of 13 identified outliers were 0.174 ± 0.166 and 0.459 ± 0.508, respectively (P = .0326). For the rectum, the respective mean sum of squared residuals were 0.103 ± 0.120 and 0.150 ± 0.171 for case-based and regression prediction (P = .1972). By retaining novel cases, under the Ample scenario, significant statistical improvement was observed over the Scarce scenario (P = .0398) for the bladder model. We expect that the incorporation of case-based reasoning that judiciously applies appropriate predictive models could improve overall prediction accuracy and robustness in clinical practice.
Type
Journal article
Subject
case-based reasoning
knowledge modeling
prostate cancer
radiation therapy
Permalink
https://hdl.handle.net/10161/19363
Published Version (Please cite this version)
10.1177/1533033819874788
Publication Info
Sheng, Yang; Zhang, Jiahan; Wang, Chunhao; Yin, Fang-Fang; Wu, Q Jackie; & Ge, Yaorong (2019). Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study. Technology in cancer research & treatment, 18. pp. 1533033819874788. 10.1177/1533033819874788. Retrieved from https://hdl.handle.net/10161/19363.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Wang

Chunhao Wang

Assistant Professor of Radiation Oncology
Deep learning methods for image-based radiotherapy outcome prediction and assessment Machine learning in outcome modelling Automation in radiotherapy planning and delivery
Wu

Qingrong Wu

Professor of Radiation Oncology
Yin

Fang-Fang Yin

Professor in Radiation Oncology
Stereotactic radiosurgery, Stereotactic body radiation therapy, treatment planning optimization, knowledge guided radiation therapy, intensity-modulated radiation therapy, image-guided radiation therapy, oncological imaging and informatics
Alphabetical list of authors with Scholars@Duke profiles.
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